Advisor - Software Engineer

Eli Lilly Eli Lilly · Pharma · Hyderabad, India

Advisor Software Engineer (R5) role focused on designing and building agentic AI systems, intelligent automation pipelines, and next-generation software platforms. This is a hands-on technical leadership role involving system design, full-stack engineering, and championing AI-driven development practices within a healthcare company.

What you'd actually do

  1. Own end-to-end system design of large-scale, distributed, cloud-native platforms – from API contracts and data models to deployment topology and observability.
  2. Write production-quality code across the stack using modern languages and frameworks (e.g., Python, JavaScript/TypeScript, Go, Java, Rust, or equivalent) on both frontend and backend.
  3. Architect and build agentic AI systems that autonomously execute multi-step workflows – leveraging LLM orchestration frameworks (LangChain, LangGraph, CrewAI, or equivalent), tool-use patterns, and retrieval-augmented generation (RAG).
  4. Act as the technical conscience across multiple teams and functions – challenging the status quo and providing recommendations to improve processes, tooling, and practices.
  5. Coach engineers at all levels, sharing specialised knowledge in architecture, AI, and software craftsmanship.

Skills

Required

  • System design and distributed architecture
  • Cloud-native platforms (AWS preferred)
  • Infrastructure-as-code (CloudFormation, CDK, Terraform)
  • Containerisation (Docker, Kubernetes)
  • Domain-driven design (DDD)
  • CQRS
  • API gateway patterns
  • System decomposition strategies
  • Modern programming languages (Python, JavaScript/TypeScript, Go, Java, Rust)
  • Full-stack development
  • Frontend frameworks (React, Angular, Vue)
  • Backend development
  • Database technologies (relational, NoSQL)
  • Testing discipline
  • CI/CD automation
  • DevSecOps principles
  • Agentic AI systems architecture
  • LLM orchestration frameworks (LangChain, LangGraph, CrewAI)
  • Tool-use patterns
  • Retrieval-Augmented Generation (RAG)
  • Prompt engineering
  • Model evaluation
  • Responsible AI practices
  • AI-assisted development tools
  • Evaluation of emerging AI technologies

Nice to have

  • GCP/Azure experience
  • Cloud certifications
  • GraphQL
  • Real-time communication layers
  • High-throughput data pipelines
  • Columnar databases
  • Multimodal AI
  • MCP
  • A2A protocols

What the JD emphasized

  • agentic AI systems
  • intelligent automation pipelines
  • AI-first mindset
  • agentic AI systems
  • LLM orchestration frameworks
  • retrieval-augmented generation (RAG)
  • prompt engineering
  • model evaluation
  • responsible AI practices
  • AI-assisted development tools
  • emerging AI technologies
  • large-scale distributed systems
  • AI-powered applications
  • agentic workf

Other signals

  • agentic AI systems
  • intelligent automation pipelines
  • AI-first mindset
  • LLM orchestration frameworks
  • RAG
  • prompt engineering
  • model evaluation
  • responsible AI practices
  • emerging AI technologies